Local Causal Discovery Towards High‐Dimensional Streaming Features

Causal discovery focuses on identifying a target's direct causes and effects (e.g., class label) feature of interest in a Bayesian network (BN). Existing causal discovery primarily includes global and local learning algorithms, which must access the whole feature space before the learning process starts. However, many real‐world applications continuously generate features in real‐time and demand stream processing of features for just‐in‐time decision‐making. In addition, the local and global learning algorithms either emphasise accuracy or computing efficiency over both. Therefore, to address these problems and handle dynamic high‐dimensional feature space, we proposed a novel local causal discovery algorithm based on streaming features that consider improving and balancing both computational efficiency and prediction accuracy denoted as Local Causal Discovery Towards High‐Dimensional Streaming Features LCDSF$$ \left({LCD}_{SF}\right) $$ . More specifically, to attain this objective, LCDSF$$ {LCD}_{SF} $$ dynamically integrates V$$ \mathcal{V} $$ ‐ and N$$ \mathcal{N} $$ ‐structure to learn the Markov blanket (MB) and simultaneously distinguishes the direct causes (parents) from direct effects (children) and parents–children (PC) from spouses of the target feature. Thus accomplishes the balance between efficiency and accuracy of prediction. The proposed algorithm has been extensively evaluated on 10 benchmark BNs and 10 real‐world datasets. The results show that the proposed algorithm outperformed the existing state‐of‐the‐art baseline algorithms. The source code is available at: https://github.com/vickykhan89/LCDSF.

• Publication year

  2025

• Venue

  Expert Syst. J. Knowl. Eng.

• Publication date

  2025-11-21

• Fields of study

  Computer Science

• Identifiers
  DOI 10.1111/exsy.70170
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Discovering optimal Markov blanket for high-dimensional streaming features

  2025cited by this paper
• Heart Disease Prediction Using Machine Learning Algorithms

  2024cited by this paper
• Local causal structure learning for streaming features

  2023cited by this paper
• Online Multi-Label Streaming Feature Selection With Label Correlation

  2023cited by this paper
• Causal Feature Selection With Efficient Spouses Discovery

  2023cited by this paper
• Causal Discovery from Temporal Data: An Overview and New Perspectives

  2023cited by this paper
• RETRACTED ARTICLE: Feature selection techniques in the context of big data: taxonomy and analysis

  2022cited by this paper
• Online feature selection for multi-source streaming features

  2022cited by this paper
• Error-aware Markov blanket learning for causal feature selection

  2022cited by this paper
• A framework for feature selection through boosting

  2022cited by this paper
• On Pearl’s Hierarchy and the Foundations of Causal Inference

  2022cited by this paper
• Causal Structure Learning: A Combinatorial Perspective

  2022cited by this paper
• A Survey on Causal Discovery: Theory and Practice

  2022cited by this paper
• Feature selection for online streaming high-dimensional data: A state-of-the-art review

  2022cited by this paper
• Online Markov Blanket Learning for High-Dimensional Data

  2022cited by this paper
• A Simple Unified Approach to Testing High-Dimensional Conditional Independences for Categorical and Ordinal Data

  2022cited by this paper
• A Data-Characteristic-Aware Latent Factor Model for Web Services QoS Prediction

  2022cited by this paper
• Additive noise model structure learning based on rank correlation

  2021influential reference
• Using Feature Selection for Local Causal Structure Learning

  2021cited by this paper
• Estimation of body fat percentage using hybrid machine learning algorithms

  2021cited by this paper
• Towards Efficient Local Causal Structure Learning

  2021cited by this paper
• D’ya Like DAGs? A Survey on Structure Learning and Causal Discovery

  2021cited by this paper
• A survey of Bayesian Network structure learning

  2021cited by this paper
• Improving Gradient-based DAG Learning by Structural Asymmetry

  2021cited by this paper
• Online Learning in Variable Feature Spaces with Mixed Data

  2021cited by this paper
• Streaming feature selection algorithms for big data: A survey

  2020cited by this paper
• Towards efficient and effective discovery of Markov blankets for feature selection

  2020cited by this paper
• Learning LWF Chain Graphs: A Markov Blanket Discovery Approach

  2020cited by this paper
• Online Streaming Feature Selection via Multi-Conditional Independence and Mutual Information Entropy

  2020cited by this paper
• Multi-Source Causal Feature Selection

  2020influential reference
• Review of Causal Discovery Methods Based on Graphical Models

  2019cited by this paper
• nuScenes: A Multimodal Dataset for Autonomous Driving

  2019cited by this paper
• Efficient Local Causal Discovery Based on Markov Blanket

  2019influential reference
• DAG-GNN: DAG Structure Learning with Graph Neural Networks

  2019influential reference
• Causality-based Feature Selection

  2019cited by this paper
• A Unified View of Causal and Non-causal Feature Selection

  2018cited by this paper
• Search for Causal Models

  2018cited by this paper
• Online Streaming Feature Selection via Conditional Independence

  2018cited by this paper
• Streaming feature-based causal structure learning algorithm with symmetrical uncertainty

  2018cited by this paper
• Local Causal Discovery of Direct Causes and Effects

  2015influential reference
• Scalable and Accurate Online Feature Selection for Big Data

  2015cited by this paper
• Discovering and orienting the edges connected to a target variable in a DAG via a sequential local learning approach

  2014cited by this paper
• Online Feature Selection with Streaming Features

  2013cited by this paper
• Exploring Causal Relationships with Streaming Features

  2012cited by this paper
• The max-min hill-climbing Bayesian network structure learning algorithm

  2006influential reference
• Wrappers for Feature Subset Selection

  1997cited by this paper

• No citing papers are available for this paper.